Impact of Facebook Live Streaming Advertisement on Purchase Intention

Chapter 1: Research Overview

1.0 Introduction

This research is to investigate the impact of Facebook live streaming advertisement on purchase intention. The research background, problem statement, research objective, research question, hypothesis of study, and research significance will be discussed in this chapter.

1.1 Research Background

The current world is at its peak of the technological era where anyone can buy anything online at the comfort of their homes according to Sharma, Shamkuwar, and Singh (2019). Social media, one of the rising technological content creations, has made it easy to share ideas and communication while at the same token shops or companies to reach their clients faster without wasting a lot of their resources (Lies, 2019). This has made many companies jump into the social media platform where not only do the firms provide social comfort but make it easy to run their business and likes. An excellent example of such web hosting companies includes Facebook, Twitter, Instagram, among other famous social network platforms according to Singh and Singh, (2018). The social network has revolutionised the way traditional markets work and thus influencing a lot of customers to consider price coverage of products sold while juggling on which fits their cards (Delacroix et al., 2019). These social changes alter customers’ attitudes on traditional shopping and product selection and the fairness of the price (Melović et al., 2020). Also, other customers’ roles and product reviews motivate customers’ choice of products as they display correct reviews. This allows one to have relevant information on which products to consider. Similarly, endorsers play a crucial role in online marketing as they aid companies to brand. Singhal and Tripathy (2019) argue that endorsers aid companies in building their brands, thereby helping in marketing products efficiently.

Maintaining trust is the far most critical concept when dealing with customers (Dzimińska et al., 2018). In the e-commerce sector, trust affects buyers’ intention to buy as they don’t meet in person with the sellers (Fauzi, 2021). Low interaction with the seller brings trust issues about whether the product purchased meets the client’s attitude and taste. This fear will make customers hesitant to buy a particular product because it will not meet their standard. In case of any inconveniences, the company will be ready to compensate. Though commerce on social networks is a new thing, people find it risky and consider electronic websites that they find easy to reach in case of anything (Zeebaree et al., 2020). Thus, taking a social tour on customers’ intentions and trust as an influential social platform is of high value.

Moreover, social network platforms like Facebook live and video clips entice their customer’s trust while developing better customer relationships. Facebook live or posts allow e-retailers to display their latest products, and if customers are interested, retailers respond to their clients instantly (Cuomo et al., 2020). This way, e-retailers will capture their clients’ trust and sell out a very positive brand for the good service displayed. In addition, Facebook live marketing will shift the traditional shopping experience to a more robust and digital way while at the same time reaching a lot of customers.

Social network marketing like Facebook boosts customers’ morale in purchasing products according to Xu, Yao, and Toe (2020). Customers choose between products that fit their description either through the product information or a review they have heard or read. This will aid in capturing customers’ intentions and thus increase online purchase intentions (Klein et al., 2019). Another factor that will influence online customers’ preference in purchasing products on Facebook is though the number of ads that play, occasionally reach out to e-retailers while at the same time showcasing how these products perform (Quadros, 2021). These acts can be made real by having videos of clients putting on or responding to how they are working with their purchased products.

The popularity of online shopping has attracted attention from researchers as it eludes a lot of spheres in reaching clients (Lu et al., 2020). Online video advertising has a broad reaching impact on the Internet and gives tremendous prospects for commercial businesses. The income of online service providers come largely from ads. This research mainly focuses on Facebook live advertising.

1.2 Problem statement

The internet has revolutionised the way things work; internet advertising is one of them. Social networking and marketing entail business promotion through social media platforms like Facebook. The social network has also established companies to sell products, build relationships, and understand the market need through social and psychological factors like attitude.

According to Wongkitrungrueng, Dehouche, and Assarut (2020), having a good relationship with clients means a lot as it allows room for clients to raise any complaints without any problem. A good case is when a client purchases a product and later on realises the product has an issue. It’s upon the company to ensure that the product purchased is compensated with the right products and conduct. Also, making sure that every complaint raised is addressed instantly helps in building trust and building a better relationship with clients. And according to Donthu and Gustafsson (2020), a relationship with customers helps marketing organisations to correct most of the arising issues to avoid any events from recurring. Chandrasekhar, Gupta, and Nanda (2019), also did a study on human psychology and found out that any issue found in a company’s products builds a bad reputation of the company and thus losing clients.

Yadav and Rahman (2018) conducted a social media attitude study on social media as an e-commerce centre. They revealed that social networks play a crucial role in many companies that have turned into the e-commerce sector. Bakri, Habidin, and Masrom (2021) also studied consumers’ attitudes towards e-retailing. The study findings agree that consumers find it hard to trust things they cannot touch or have experienced on the first site. Adekannbi (2020) further conducted a study on mobile advertisement of products, but the study did not provide any information on its effectiveness in influencing buyers. Chen and Wang (2020) surveyed product attitude through advertised live streaming and the kind of trust developed through elaborate likelihood models. The study findings showed more than one route where consumers’ attitudes in purchasing products greatly influence product description and advertisement. Wongkitrungrueng, Dehouche, and Assarut (2020) conducted a study on live streaming from sellers’ perspectives. The findings showed that the e-retailers approach constantly determines consumers’ attitudes toward buying or recommending other clients on the same product. Chu, Kamal, and Kim (2019) also conducted a study on determining consumers’ response toward social networking platforms and intentions in buying luxury products.

Mayrhofer, Matthes, Einwiller, and Naderer (2020) studied brand selling on young adults’ purchase intentions using social media. In summary, a lot of studies have been done on social network advertisements, but less has been done on social media live streaming advertisements like Facebook live. Lastly, the study will try to unfold other major problems that affect livestreaming marketing like the content created, brands, accessibility in reaching clients among other factors. Therefore, this study will try to unfold and cover the knowledge gap on live streaming advertisement based on consumers’ attitudes.

1.3 Research Objective

The aim of this research is to identify the impact of Facebook live streaming advertisement on purchase intention. There are two types of research objectives which are general research objective and specific research objective.

1.3.1 General Research Objective

To investigate the relationship of Facebook live streaming advertisement on consumers’ purchase intention.

1.3.2 Specific Research Objective

  1. To investigate the influences of informativeness of Facebook live streaming advertisement on consumer’s purchase intention.
  2. To investigate the influences of entertainment of Facebook live streaming advertisement on consumer’s purchase intention.
  3. To investigate the influences of irritation of Facebook live streaming advertisement on consumer’s purchase intention.
  4. To investigate the influences of credibility of Facebook live streaming advertisement on consumer’s purchase intention.

1.4 Research Question

  1. Does the informativeness of Facebook live streaming advertisements influence the consumer’s purchase intention?
  2. Does the entertainment of Facebook live streaming advertisements influence the consumer’s purchase intention?
  3. Does the irritation of Facebook live streaming advertisements influence the consumer’s purchase intention?
  4. Does the credibility of Facebook live streaming advertisements influence the consumer’s purchase intentions?

1.5 Hypothesis of Study

H1: There is a significant relationship between the influences of informativeness of Facebook live streaming advertisement on consumer’s purchase intention.

H2: There is a significant relationship between the influences of entertainment of Facebook live streaming advertisement on consumer’s purchase intention.

H3: There is a significant relationship between the influences of irritation of Facebook live streaming advertisement on consumer’s purchase intention.

H4: There is a significant relationship between the influences of credibility of Facebook live streaming advertisement on consumer’s purchase intention.

1.6 Research Significance

This study is to examine the impact of Facebook live streaming advertisement on purchase intention. The main focus of research is how the impact factors informativeness, entertainment, credibility and irritation of Facebook live streaming advertisement on consumer’s purchase intention. To achieve the research target, our research takes into account both antecedents and consequences of our clients engaging in the context of live shopping. Our research also provides some practical advice for sellers and e-commerce platforms to better use live streaming to effectively market their product (Sun, Shao, Li, Guo, & Nie, 2019). Furthermore, the value of this research is to add more knowledge to the existing literature on the topic and help readers to add more documentation on the relationship between live streaming and consumer purchase intention. This research  also helps to better understand the purchase intention of consumers adopting new brands through Facebook live streaming advertisement.

1.7 Conclusion

In short, the summary of this chapter had included a brief overview on the background of the study which discussed the impact of Facebook live streaming on purchase intention. The research problem provides a direction for the researcher to analyse and identify. With the research objectives which provide an outline on the goals to be achieved. Besides, the study significantly establishes the importance of this research. Further studies will be reviewed more on the related literatures and conceptual framework models in the next chapter.


Chapter 2: Literature Review

2.0 Introduction

This chapter will cover the underlying theories of the study. This chapter also describes the independent and dependent variables and formulates a hypothesis based on the variables linked to Facebook live streaming advertisements purchase intention.

2.1 Review of Relevant Theoretical Model

Ducoffe’s Web Advertising Model

According to Yang et al., (2017) advertising value is a notion established by Ducoffe to quantify how much people value advertising. Ducoffe conducted research that led to the development of a model based on the triad of informativeness, amusement, and aggravation as antecedents of perceived worth. According to Fu et al., (2020), the first step in making effective use of online advertising is to learn how audiences feel about the Web’s worth as a source of consumer information. This feeling, in turn, affects how they feel about specific commercials on the Web. However, knowing who you’re writing for isn’t enough. After this is complete, only then can the most relevant advertisements for the web be created. As a result, boosting advertising value has emerged since a pressing concern, as it influences how well online ads perform and how their intended viewers perceive them. As per Chakraborty, (2019), Ducoffe showed that people’s opinions of ads on the Internet depended critically on how much they believed such ads would help them. Therefore, for practitioners who want to build the most successful online advertisements directed at their target clients, disclosing the features of web ads has become a crucial problem. Commercials’ informativeness is measured by how much information they are able to carry along to their intended audience. Ducoffe created a paradigm for anticipating consumer value and attitude toward advertising by disentangling its emotional component from its cognitive reaction. He concludes that consumers’ perceptions of ad values, and hence their attitude toward commercials, are affected by their level of enjoyment, education, and annoyance. When it comes to the web, Ducoffe’s model is what ends up being used (Shareef et al., 2019). Without differentiating between the two sorts of advertising aims, he shows that advertising value is strongly related to how people feel about online ads. Furthermore, the value of advertising depends on how entertaining, instructive, and irritating it is judged to be.

Figure 1: Ducoffe’s Web Advertising Model (Yang et al., 2017)

2.2 Review of Variables

2.2.1 Dependent Variable: Purchase Intention

Researchers in the fields of marketing and information systems often utilize consumers’ stated desire to continue using a service as a proxy for their actual behavior (Chetioui et al., 2021). The success of a service provider depends on the extent to which its customers continue to use the service. Consumers’ propensity to make a purchase is measured by their “purchase intention” (Chetioui et al., 2021). According to research Chu, Kamal, and Kim, (2019), customers’ propensity to buy is a strong indicator of their actual purchasing behavior. A consumer’s readiness to buy a product or brand is described as an unbiased preference for that product or brand (Liu et al., 2020). Nonetheless, customers’ preferences for certain items are what “willingness to purchase” is all about, as stated by (Jain, Rakesh, & Chaturvedi, 2018). Some research has found that the length of time between purchases is a good indicator of consumers’ propensity to buy a certain product. However, it might be difficult to determine whether or not a desired outcome (a purchase or a referral), actually occurred in empirical investigations. Consumers consider their personal experiences and the surrounding environment when making purchasing decisions. After collecting a given amount of data, consumers begin to examine, think about, and compare products in preparation for making a purchasing decision. Consumers’ subjective preference for a given product may be measured by their purchase intentions (Mayrhofer et al., 2020). There is a direct correlation between a consumer’s level of interest in a product and their chance of making a purchase of that product (Chakraborty, 2019). Previous studies have found that consumers’ perceived propensity and their actual behavior may both be predicted with high accuracy using purchase intention. According to Zhang et al., (2020), three factors’ objectives must be taken into account if a firm wants to keep a community going and meet community goals while also making effective marketing through the community. The first factor is the likelihood of a member sticking around, which reflects their commitment to the group and their satisfaction with being a part of it. The second factor is members’ intentions to suggest the community to those who aren’t already part of it. Third, members’ propensity to take part in community events is reflected by their intention to do so. Therefore, this study aimed to investigate the impacts of social media marketing activities on participation intention and purchase intention, as opposed to the effects of information system utilization, as was the case in prior studies. In the end, the success or failure of any and all marketing efforts will hinge on one thing: whether or not potential buyers are interested in the goods. Attitudes are found to positively affect consumers’ buying intentions and final decisions (Saima & Khan, 2020). Attitude is defined as the expression of liking or disliking an item and is connected to the ideas of belief and behaviour (Liu et al., 2020).

Social media marketing has been shown to affect consumers’ propensity to make a purchase (Chetioui et al., 2021). Chakraborty, (2019) looked at how social media marketing affected consumers’ intent to buy a certain product. Scale creation and validation for measuring consumer reaction to e-commerce social media marketing initiatives; findings support the hypothesis that SMM initiatives have a beneficial effect on consumers’ propensity to make a purchase. To promote their goods and services, businesses often employ a tactic known as “social media marketing activities,” which involves tapping into the networks’ user bases. A purchasing decision is defined as the picking of an action or alternative from a set of alternatives (Wongkitrungrueng et al., 2020). There must be some variety of choices available to consumers before they will commit to one. According to Chetioui, Butt, and Lebdaoui, (2021), the most common kind of customer confidence manifests itself in the form of a conviction that the consumer’s choice of a certain product was the optimal choice. When customers are conscious of the internal and external influences on their purchasing decisions, they actively seek out information to help them make educated purchases.

2.2.2 Independent Variable 1: Informativeness

Informativeness refers to the potential of advertising to provide information to customers about goods or services (Cahyani et al., 2020). Informativeness is a significant component in determining the efficiency of online advertising. The primary goal of informativeness is to attract consumers by providing them with current, accurate, and easily available information (Ling et al., 2018). Consumers’ attitudes regarding Internet advertising appear to be positively related, as Murat, Sibel, and Ceyda (2016) found. As a result, marketing relies heavily on information as an incentive since consumers are more likely to click on adverts that give incentives (Wang et al., 2017).

The significance of informativeness may be perceived from an instrumental or utilitarian approach. Information on the functional characteristics of a product or service may help customers’ functional demands in a significant manner (Aktan et al., 2016).  Therefore, the quality of information offered in advertisements, such as the advantages of a product, may assist people make optimum purchases (Ku et al., 2019). Moreover, the significance of advertising innovation is linked with the underlying human need to consume anything novel, unique, unconventional, and innovative (Aktan et al., 2016). Understanding these factors will provide advertising practitioners with useful insights into how best to create messages that can generate positive attitudes to advertising (Ku et al., 2019). Specifically, research has found that the more informative and creative users perceive an advertising message to be, the more positive their attitude to it is (Ku et al., 2019). Thus, users are likely to develop a positive attitude to advertising when it provides information that addresses their functional needs and when it is creative enough to provide hedonic value (Murillo, et al., 2016). Therefore, to increase the likelihood that users will react favourably, advertisers and marketers should make their advertising messages more informative and creative (Ku et al., 2019).

People frequently use social media as a way to get information (Najib et al., 2016). Adverts on social media may supply consumers with information about a product they’re interested in and pique their interest (Wang et al., 2017). Customers will derive value from the sense of the importance of SMA information (Taanika et al., 2019). It is hypothesised that a link between the conceptual framework as well as social media marketing might be tested in this analysis. There is a considerable influence on the perceived usefulness of social media marketing when an advertisement is informational (Taanika et al., 2019).

2.2.3 Independent Variable 2: Entertainment

The concept “entertainment” defined as the degree to which Web advertisements satisfy an audience’s need for escape, diversions, aesthetic pleasure, or emotional expression (Shareef et al., 2017). Consumers want or choose advertising that include aspects of fun and enjoyment. Web advertising that is heavily loaded with enjoyable material receives a better rating from receivers and results in a greater target to return to the homepage than websites that lack entertainment aspects (Aydoğan et al., 2016). According to Hossam (2018), when advertising presented on social networking sites have entertaining value, the advertisement’s value increases. This research tried to establish entertainment as hedonic in nature (Cadet et al., 2017).

Advertising has historically been sold as being in support of entertainment, rather than as a true source of entertainment for consumers in its own right (Goh et al., 2020). Even when those advertisements are creative in their own right or license the most popular entertainment properties of the moment, it is seen as something separate from consumer-focused entertainment content (Goh et al, 2020). Younger consumers are more concerned with experience than products than previous generations, and brands are shifting to prioritising the creation of long-term relationships through providing those experiences over short-term sales (Aktan et al., 2016). In those cases, the advert is arguably a piece of entertainment in its own right.

Feelings of enjoyment caused by commercials play the largest role in generally taking into account their attitudes towards advertising (Ismail et al., 2022). Throughout their contact with computer- based media a high degree of enjoyment and participation contributes to convergent subjective impressions of the consumer’s positive influence and mood (Aktan et al., 2016). The entertainment interest of advertisement knowledge is related to conventional advertisements. The advertising message’s entertainment value has a favourable impact on the attitude of consumers toward ads (Ganjar et al., 2018). The degree to which promotional knowledge is entertained is important for advertisements on social networking sites. An exciting advertising message can influence attitudes towards it by consumers (Hashim et al., 2018).

2.2.4 Independent Variable 3: Irritation

Certain advertisements are annoying to consumers because they are seen as deceptive, rude, or a slap in the face to their intelligence (Jain et al., 2018). Consumers may find the message irritating because it is hard to understand, invasive, or distracting. Therefore, annoyance is said to be a significant cause why people have a negative outlook on commercials (Chetioui et al., 2021). Users’ irritation with ads is also thought to reduce the benefits they receive from them (Fu et al., 2020). Since mobile phones are undeniably highly private gadgets, the annoyance element may be especially relevant while reading messages sent to them (Liu et al., 2020). In practice, researchers have discovered that intrusiveness of the message and irritation is a significant factor that negatively affects attitudes towards mobile advertisements (Chetioui, Butt, and Lebdaoui, (2021; Mayrhofer et al., 2020; Chu, Kamal, and Kim, 2019; Zhang et al., 2020).

The annoyance that customers feel in response to some forms of advertising is what we mean by the term “irritation” (Saima and Khan, 2020). The most common complaint about ads is that they are annoying (Liu et al., 2020). The potential for mobile advertising to annoy consumers was once thought to be substantial. Consumers are more likely to view advertisements negatively when they use methods that irritate, insult, or are extremely manipulative (Jain et al., 2018). It was later argued by Wongkitrungrueng, Dehouche, and Assarut, (2020) that the general public was inexperienced with mobile commerce and doubted the viability and safety of mobile advertising because of these misconceptions. In addition, customers are irritated when bombarded with too many ads from a single service provider. Others have recently stated that the annoyance of SMS has less of an effect on attitudes in a mobile situation now that smartphones have improved (Jain, Rakesh, and Chaturvedi, 2018; Chakraborty, 2019; Wongkitrungrueng, Dehouche, and Assarut, 2020).

Irritation, a concept articulated by Ducoffe’s model, refers to the aggravating features of advertising such false promises, contradictory information, inappropriate content, and intrusive calls to action (Chakraborty, 2019). IRR of advertising has been linked to factors such as ad content, ad format, the online platforms on which ads appear, customer familiarity with and response to web ads, and other factors, according to both historical and modern studies (Jain et al, 2018). Previous research indicates that IRR has a detrimental impact on the value of online advertising and the quality of the user experience associated with it. The value of product placement was more reactive to discomfort than that of traditional advertising (Chetioui et al., 2021). A prior study found that using continually animated site banners and/or unexpected popup adverts increased visitors’ perceived aggravation with the website (Chetioui et al., 2021). Negatively impacting felt annoyance for ordinary customers is shocking implementation of visual website design, website navigation, and information strategy. For instance, the impact of Facebook advertising IRR on user sentiment about the platform is significant (Saima and Khan, 2020). However, the IRR of SMS advertising was strongly correlated with the time of day they were received. One more study came to the conclusion that digital billboards’ return on investment (IRR) and audience satisfaction are moderated by their physical location (Zhang et al., 2020). An individual’s point of incremental return (IRR) from internet advertising is correlated with his or her level of web knowledge. In reality, internet natives do not often find online ads annoying (Jain, Rakesh, and Chaturvedi, 2018). Online shoppers’ levels of frustration were shown to be most impacted by a site’s intuitive navigation structure. Sponsored links are seen negatively due to IRR (Wongkitrungrueng et al., 2020). Attitude toward the place has a strong negative effect on IRR. However, (Fu et al., 2020) found the exact reverse, reporting that annoyance actually encouraged smartphone purchases in Bangladesh. However, this study has evaluated a positive connection between IRR and PI in light of the peculiarities of the Bangladeshi client base.

2.2.5 Independent Variable 4: Credibility

According to the research, trust in commercials is a major element in how people feel about them. According to Chetioui, Butt, and Lebdaoui, (2021), “the extent to which the consumers consider the statements about the brand/product represented in the advertising to be genuine and convincing” is an antecedent that influences one’s attitude toward commercials. When assessing an advertising’s veracity, it is important to consider both the veracity of the advertisement itself and the veracity of its source (the firm or organization paying for the advertisement) (Mayrhofer et al., 2020). Attitudes toward advertisements are affected by consumers’ beliefs about their accuracy, reliability, and credibility (Mayrhofer et al., 2020; Chu, Kamal, and Kim, 2019; Zhang et al., 2020; Liu, Zhang, and Zhang, 2020; Saima and Khan, 2020).

Lastly, the study takes into account the credibility or trustworthiness of advertisements as an independent variable. As described by Ducoffe, credibility includes being trustworthy, credible, persuading, and providing truthful product information (Zhang et al., 2020). Consumers’ assessments of INFO, ENT, and IRR are all strong indicators of confidence in online retailers, and respondents’ judgments of an ad’s credibility were shown to be the most influential element in shaping their attitudes about marketing messages. Consumers have a somewhat pessimistic view of the reliability of SMS ads (Chakraborty, 2019). They found that consumers’ attitudes regarding SMS ads shifted for the worse. The SMS advertising’s efficacy was affected by the credibility of the ad’s source. It’s possible that the ambiguity of not being asked to “opt-in” while getting advertising lowers the efficacy of SMS marketing when credibility is low, and raises it when credibility is high (Fu et al., 2020). It’s possible that adding an opt-in feature might lend legitimacy, making the advertisement more successful (Drossos et al., 2017). When it comes to how young people feel about receiving SMS advertisements, Wongkitrungrueng, Dehouche, and Assarut, (2020) say that believability matters a great deal. When it comes to advertising, users care most about the reliability of sponsored links.

There is a consensus among researchers that exposure to advertisements, as well as the credibility of the advertiser and the degree to which the advertisements are annoying, are major factors in shaping consumer attitudes about advertising (Chetioui et al., 2021). People’s opinions of advertising are heavily influenced by their level of trust in both the ad and the company delivering it (Jain et al., 2018). When compared to print media or television commercials, the regulatory systems of digital media are laxer, hence the trustworthiness of the advertising is likely to have a greater impact on consumer perceptions of ads. Research on commercials, both in traditional and mobile settings, increasingly takes into account the annoyance aspect. There is a growing backlash against advertisers due to their annoying and, in some cases, disrespectful commercials. Therefore, relevant research have shown that the annoyance construct negatively affects ad sentiments (Fu et al., 2020). All of the above-mentioned elements, which are known as antecedents of attitude, have been accounted for in this study and will be examined in further detail below.

2.3 Proposed Conceptual Framework

Based on the literature and Theoretical Model, we had proposed a research framework shown below. There are four IV’s for this research study which are informativeness, entertainment, credibility and irritation. Besides, for the result of our research, DV would be the Facebook live streaming advertisement on purchase intention.

Figure 2.3.1 Proposed Research Framework

Source: Developed from Research 

2.4 Hypotheses Development

2.4.1 The relationship between Informativeness and Purchase Intention

Generally, informativeness refers to the extent to which a company can provide sufficient information so that customers can make better buying decisions (Alalwan, 2018). According to Filieri, McLeay, Tsui & Lin (2018), informativeness on social commerce platforms are considered helpful if the information provided helps consumers familiarise, understand, and evaluate the quality and performance of products sold online. Informativeness is a key factor in consumer acceptance of advertising. It plays an important role in advertising awareness of a product and helps consumers differentiate a product from existing competitors. Informational advertising can inform consumers of new product features and changes in product prices. Therefore, informativeness in an advertisement can strongly influence consumer attitudes, especially when it is repositioned from traditional media (Goh, Ang, Tan & Oun, 2020). Based on the Rajeev, Archi & Dipti (2018) research, it shows the value of online advertising is now dependent on its informativeness, credibility and entertainment. The research also shows that informativeness has an influence on consumer attitude towards advertising, and attitudes mediating between the perceived advertising value and purchase intention.

H1: Informativeness of Facebook live streaming advertisement has positive influences on consumer purchase intentions.

2.4.2 The relationship between Entertainment and Purchase Intention

According to the Lapatsanan (2017), entertainment intention is an experience that are enjoyable, memorable, relevant and appreciation will stay in memory and influence customer’s attitude and next purchase intention. To be specific and important, entertainment plays an important role as it determines respondents’ attitudes towards live streaming advertising. Based on the Le & Vo (2017) study, entertainment is one of the values ​​of media. Therefore, as an important form of media, advertising can improve consumers’ advertising transaction experience if it can deliver entertaining content to viewers. Moreover, entertainment can be considered as an important predictor of advertising value besides being a key factor in online advertising. It means that entertainment is a key factor that should be incorporated into advertising messages to increase the value of online advertising by attracting the interest and attention of the receivers (Murat, Sibel & Ceyda, 2016). Besides, Chen & Lin (2017) study shows entertainment has positively affects attitudes, which in turn influence recommendation willingness and intention to use a given social platform. It shows the entertainment of a website affects traffic, which in turn affects customer satisfaction and purchase intention.

H2: Entertainment of Facebook live streaming advertisement has positive influences on consumer purchase intentions.

2.4.3 The relationship between Irritation and Purchase Intention

Yang, Huang, Yang & Yang (2017) mention the irritation has the potential to divert attention from worthwhile social goals, dilute the human experience, and exploit human anxieties and hopes of affectionate possession. This may be caused by the organisation of the website that confuses and distracts consumers. Moreover, live advertising has the potential to present a wealth of information that confuses, distracts and overwhelms the recipient. As a result, consumers are confused and react negatively to the message being conveyed (Hashim, Normalini & Sajali, 2018). Based on the research from Firat (2019), it shows the respondent towards the value of online advertising has negatively affected the value of online advertising. Therefore, irritation is believed to have a negative impact on consumer purchase intention towards online advertising.

H3: Irritation of Facebook live streaming advertisement has negative influences on consumer purchase intentions.

2.4.4 The relationship between Credibility and Purchase Intention

Credibility is currently an integral part of various studies in the online environment. It can reduce the impact of consumer demand uncertainty on purchase intention and website loyalty. In a highly uncertain online environment, improving creditworthiness helps consumers reduce the importance of risk perception (Zhu, Li, Wang, He & Tian, 2020). According to Singh & Banerjee (2018) research, it shows that celebrities who appear in advertising will generate higher purchases intention. The studies also prove that when the credibility of the speaker is high, consumers may be more willing to buy more. When a credible source is used as a spokesperson for advertising, it influences consumer beliefs, assessments, attitudes and/or behaviour and can motivate consumers to accept the influence of information as accurately as possible and use it. Besides, credibility has a positive effect on the perception of advertising value. If consumers don’t perceive live stream advertisements, they may move away or not respond to advertisements that are trustworthy and don’t pay attention to the message (Martins, Costa, Oliveira, Gonçalves & Branco, 2018). Therefore, credibility has positive influences on the purchase intention of online advertisement.

H4: Credibility of Facebook live streaming advertisement has positive influences on consumer purchase intentions.


Chapter 3: Research Methodology

3.0 Introduction

This chapter will cover the methodology used to address the research questions. This chapter discusses the research design, sample design, and data gathering technique. In addition, we conduct pilot tests to see if the suggested methods can be applied to our real data collection. This chapter also describes the planned data analysis procedures to assure the accuracy and validity of the study. The data analysis methodology is also documented and consistent with the study objective.

3.1 Research Design

Marketing research is carried out within the context of a research design framework. It follows a set of predetermined actions to get the necessary data and offers a solution to marketing research challenges. Our research focuses on the impact of Facebook live streaming on purchase intention. The research design framework will conduct the research in systematic steps to gather the required information for the study (Geoffrey et al., 2019). We have chosen to conduct our analysis using the quantitative study design.

3.1.1 Descriptive Research

For this task, descriptive research is the suitable approach for the achievement of our goal. Facebook is one of the best advertising sites most people prefer since it can reach a substantial population worldwide (Andreou et al., 2019). Using Facebook live streaming for advertisements is one of the best strategies one can follow to get a successful outcome for whatever product is being advertised (Bala & Verma, 2018). Streaming live using Facebook helps millions of companies maintain good relationships with their customers (Wibowo et al., 2020). Any gap existing for unexpected reasons against the company or its services is bridged through live stream ads (Pedeliento & Kavaratzis, 2019). Consequently, a live advertisement will increase sales and profit margins (Tong, Luo & Xu, 2020). Furthermore, the approach will accrue to maintaining the existing customers and enticing any other potential customers (Wu & Li, 2018). Furthermore, live advertisements help any company increase its target market (Jamison et al., 2020). This is when a good plan for the advertisement approach is used.

Facebook live streaming is a cost-effective advertisement approach that easily reaches a large population distribution without too many expenses (Brynjolfsson, Collis & Eggers, 2019). Therefore, many of the funds that entrepreneurs could have spent on ads are used for other fruitful plans (Muhammedrisaevna, Mubinovna & Kizi, 2020). The resources required for a live stream session are only a good internet connection and a mobile phone or a computer (Aral et al., 2019). Ultimately, the live streaming advertisement technique maintains a real-time engagement with the customers (Wongkitrungrueng, Dehouche & Assarut, 2020). Real-time customer engagement motivates them to make more purchases and recommend their friends (Guo et al., 2021).


3.1.2 Quantitative Research

In quantitative research, numbers are used to explain, characterise, and predict variables and phenomena of interest (Silva, 2017). More than one billion people use the platform for various activities (Shodiyev, 2022). For instance, 1.87 billion Facebook users who have signed up can connect and interact with their friends, work colleagues, teammates, and new allies from other parts of the world (Dasgupta, 2022). Additionally, about seven million advertisements are done through the platform since it supports sharing images, video clips, audio, articles, and streaming live sessions (Todorovic et al., 2021). By using the mentioned mediums, any advertisement will guarantee a remarkable achievement that meets the entrepreneur’s or company’s expectations (Wongkitrungrueng, Dehouche & Assarut, 2020). Moreover, more than 7 million advertising businesses have gained higher reputations.

3.2 Sampling Design

Sampling design is the foundation on which a sample survey is constructed and how it influences other survey components.  

3.2.1 Target Population

These individuals or groups share similar characteristics from which researchers generate their samples (Dahabreh et al., 2022). In this study, the target population is people with the Facebook application on their devices who generally know how to connect to live streams in Malaysia. The study will be focused on individuals with social media operation knowledge and are frequent users. The participants will be limited by their proficiency in communication and understanding of the products being advertised on the live streams. Determining the target population is guided by the question; What are the people for whom you will be held accountable to achieve targeted objectives in explicit, quantifiable terms? In addition, to determine the target population, researchers focus on the demographic and assets of the participant in question.

3.2.2 Sampling Frame and Sampling Location

The sample frame would correspond to the population of interest in an ideal world. In our study, we will not use the sampling frame because the sample frame was unavailable in the research due to the absence of listings including information on Facebook users. The structure will only accommodate smartphone users, and the questionnaires and surveys will be distributed through social media platforms. The study will include old and new Facebook subscribers. Describe the diversity of the people involved. The main feature they must pose is the ability to operate Facebook live streams.

A Facebook live stream is an online performance and, therefore, with access to the internet connection, sampling can be done online. As well, interviewing of various advertising firms sampling analysis can be done. Ultimately, using Facebook live streams for advertisements is a tool that can help achieve the targeted margins of sales for any company or business (Silas & Junior, 2020). Many companies and upcoming entrepreneurs have adapted this advertising technique since it works the best (Hunt & Madhavaram, 2020). More sales and new customers are gained.

3.2.3 Sampling Elements

The general public of Malaysians have experienced watching live streaming advertisements at least once from Facebook, which would be the target respondents in this study. People of various societal statuses, such as students, workers, retirees, housewives, and others, regardless of their age, gender, race, or monthly pocket money, are included. As a result, this study can get a variety of responses on consumer satisfaction with Facebook.

3.2.4 Sampling Technique

Self-selection sampling would be the most fitting in this scenario for it does not require a sampling frame. Again, it allows us to draw solid conclusions about the entire group, which includes random convenience picking or other variables to make data collection more accessible. These are the two main sampling techniques. We will employ the Self-selection sampling technique in the study, providing all Facebook users an equal chance of being chosen to participate (Taherdoost, 2016). Additionally, self-selection was chosen since it is a rapid and cost-effective method for locating the sample.

3.2.5 Sample Size

Sample size defines the total number of individuals or the number of observations involved in a study (Lakens, 2022). The sample size addresses a group of people chosen from the general public who are deemed to represent the actual population for the study. Our study will use a sample size of 384 individuals. The sample size will be divided into smaller clusters to help reduce the chances of errors and achieve accurate estimation. The clusters will be developed using the criteria of credibility, informativeness, incentives, and purchase intentions. Data from the participants will be collected through Facebook in the form of questionnaires. The questionnaires will also be sent to the participants through emails. Krejcie and Morgan (1970) propose a sample size of 384 once the population is more than 250,000. This study focuses on 384 respondents because there were 28,947,300 Facebook users in Malaysia (Napoleon Cat, 2021). In this survey, 384 Malaysians who have registered the Facebook account were asked to fill out a questionnaire.

3.3 Data Collection Method

3.3.1 Primary Data 

Primary data collection is a method using questionnaire, survey, or interview to collect the data.  It includes data collected for the first time and also raw and fresh data. Primary data is collected when conducting experiments in experimental research, but in case of descriptive research and surveys, primary data is a form of observation or direct communication with respondents or others (Syeda, Rubi, Ammar & Abdul, 2021). In this research, we will gather the primary data through the Google form as our research survey and we will assign the questionnaire among our target respondents to gather data.

3.3.2 Questionnaire Design

In this research, the questionnaire is created as a Google form and the Google link would be sent to the respondent by Facebook, Instagram, Discord, WhatsApp, and Wechat to collect data. The English language would be applied in the questionnaire design, and the questionnaire also separated into three parts which are Section A, Section B and section C.

In Section A, 5 demographic questions are implied to gather the target respondent’s demographic background which includes age, gender, race, occupation and personal monthly income. In section B, we prepare 4 general questions to ask about the frequent watch of Facebook live streaming advertisement and number of spending in Facebook live streaming. For section C, the questions consist of IV and DV that evaluate the impact of Facebook live streaming advertisement on purchase intention. There are totally 4 IVs which are informativeness, entertainment, irritation, and credibility while the DV is the purchase intention of Facebook live streaming. Each of the IV and DV contain 5 questions that have been designed to collect the opinion on the impact of Facebook live streaming advertisement on purchase intention. Besides, the likert scale is used in the question design of section B and the scales given change from strongly disagree to strongly agree.

3.3.3 Pre-test and Pilot Test

Pretesting is to verify that the target respondent understands the question and suggested answer options as the researcher intended, and it is indeed able to respond meaningfully (Perneger, Courvoisier, Hudelson, & Gayet-Ageron, 2015). We were required to get opinions from 15 lectures to guide us in identifying errors and flaws in the questionnaires.

Pilot test is the first step in an overall research protocol and is usually a smaller study that helps in planning and revising the main study (In, 2017). We use a pilot test to test our research approach with a small number of test participants before we conduct the main study. We conducted the pilot test to determine the error from the result obtained from the test and allow us to test the correctness of the research instrument and provide the information on whether the type of survey is effective in fulfilling the purpose of the study. According to Johanson & Brooks (2010), the pilot test for initial investigation or scale development purposes, the sample size of 30 representative participants of the population of interest is a reasonable minimum recommendation. Therefore, we will acquire 30 respondents to carry out the pilot test.


3.4 Proposed Data Analysis Tool

3.4.1 Descriptive Analysis

Descriptive analysis is a data analysis that helps describe, display, or summarise data points in a constructive manner so that patterns emerge that satisfy each condition of the data. It was a method that was used to objectively describe the nature and magnitude of sensory perception (Sarah, Joanne, Tracey & Ng, 2018).  In this research, descriptive analysis was chosen to determine the data, which contains an overview of demographic data from our respondents which is illustrated in tables.

 3.4.2 Reliability Test

Reliability is related to the consistency of measurements. It can be achieved through three attributes which are homogeneity, stability, and equivalence. The reliability coefficient is an absolute number ranging from 0 to 1. A value of 1 means perfect consistency, while a value of 0.00 indicates a complete lack of consistency (Samuel, 2018). Cronbach’s alpha is the most commonly used test to determine the internal consistency of an instrument. In the reliability test, the mean of all correlations in each half-split combination was determined. Instruments with more than two response answers can be used in this test. Cronbach’s alpha result is a number between 0 and 1. An acceptable reliability score of is 0.7 and higher (Heale & Twycross, 2015).

Table 3.4.2:  Rule of Thumb for Cronbach’s Coefficient Alpha Range

Cronbach’s alpha Ranges (α) Strength of Association
α ≥ 0.9 Excellent
0.9 > α ≥ 0.8 Good
0.8 > α ≥ 0.6 Acceptable
0.7 > α  ≥ 0.6 Questionable
0.6 > α ≥ 0.5 Poor
0.5 > α Unacceptable

Source: Sharma, 2016

3.4.3 Pilot Test’s Reliability Result

Table 3.4.3 Pilot test’s reliability result

Variables No of items Standard Variable Level of Reliability
Cronbach’s Alpha
Dependent Variables (DV) Purchase Intention (PI) 5 0.916  

High reliability

Independent Variables


Informativeness (IN) 5 0.940
Entertainment (EN) 5 0.960
Irritation (IR) 5 0.966
Credibility (CR) 5 0.912

Source: Developed from research


3.4.4 Inferential Analysis Pearson’s correlation coefficient analysis

In this research, Pearson’s correlation coefficient analysis (r) is used to reflect the association or relationship between two (or more) quantitative variables (Gogtay, 2017). At the same time, the IV (Informativeness, Entertainment, Irritation and Credibility) and DV (Purchase Intention) of this research are used to test how strong of association between both variables. It can take values ​​ranging from -1 to +1. A positive value indicates a positive linear correlation, and a negative value indicates a negative linear correlation. The closer the value is to +1 or -1, the stronger the linear correlation (Fu, Tang, Cai, Zuo, Tang & Zhao, 2019).


Table Guidelines of Correlation Coefficient Range

Correlation Coefficient Strength of Correlation
0.00 – 0.10 None
0.10 – 0.39 Weak
0.40 – 0.69 Moderate
0.70 – 0.89 Strong
0.90 – 1.00 Very Strong

Source: Schober, Boer & Schwarte  (2018) Multiple Regression Analysis

Multiple regression analysis (MRA) refers to a set of correlation-based statically techniques used to measure the influences of independent variables (IV) on dependent variables (DV) (Plonsky, 2015).  The formula or equation that uses to evaluate the relationship between the variables is shown as below :

Y’=A+ B1 (X1)+ B2 (X2)+ B3(X3)+ …+Bk (Xk)

Based on the equation, Y’ represents the DV which is the purchase intention of Facebook live streaming advertisement, X1 represents informativeness, X2 represents entertainment, X3 represents irritation and X4 represents credibility. The below shown is the equation of this research:

PI = A+B1 (IF)+B2 (EN) – B3 (IR)+ B4 (CR)


PI = Purchase intention of Facebook live streaming advertisement

A = constant

Bx = each parameter estimate unit

IF = Informativeness

EN = Entertainment

IR = Irritation

CR = Credibility

When there is an increase for every unit in informativeness, entertainment, irritation and credibility, there will be an increase for each of the parameter estimated units.